Data Engineer - Dallas, TX

Posted 21 Days Ago
2 Locations
In-Office or Remote
38K-133K Annually
Mid level
Agency • Information Technology
The Role
Design and build RAG-focused data pipelines for LLMs: ETL/ELT for structured and unstructured data, vector database architecture, embedding and chunking strategies, data cleaning/PII removal, metadata engineering, low-latency streaming, and evaluation/versioning to support autonomous agents.
Summary Generated by Built In

We are seeking a Data Engineer who will be responsible for the "Ingestion-to-Insight" pipeline that allows autonomous agents to access, search, and reason over vast amounts of proprietary and public data.

Your role is critical: you will design the RAG (Retrieval-Augmented Generation) architectures and data pipelines that ensure our agents have the right context at the right time to make accurate decisions.

Key Responsibilities

  • AI-Ready Data Pipelines: Design and implement scalable ETL/ELT pipelines that process both structured (SQL, logs) and unstructured (PDFs, emails, docs) data specifically for LLM consumption.
  • Vector Database Management: Architect and optimize Vector Databases (e.g., Pinecone, Weaviate, Milvus, or Qdrant) to ensure high-speed, relevant similarity searches for agentic retrieval.
  • Chunking & Embedding Strategies: Collaborate with AI Engineers to optimize data chunking strategies and embedding models to improve the "recall" and "precision" of the agent's knowledge retrieval.
  • Data Quality for AI: Develop automated "Data Cleaning" workflows to remove noise, PII (Personally Identifiable Information), and toxicity from training/context datasets.
  • Metadata Engineering: Enrich raw data with advanced metadata tagging to help agents filter and prioritize information during multi-step reasoning tasks.
  • Real-time Data Streaming: Build low-latency data streams (using Kafka or Flink) to provide agents with "fresh" data, enabling them to act on real-time market or operational changes.
  • Evaluation Frameworks: Construct "Gold Datasets" and versioned data snapshots to help the team benchmark agent performance over time.

Required Skills & Qualifications

  • Experience: 4+ years in Data Engineering, with at least 1 year focusing on data for LLMs or AI/ML applications.
  • Python Mastery: Deep expertise in Python (Pandas, Pydantic, FastAPI) for data manipulation and API integration.
  • Data Tooling: Strong experience with modern data stack tools (e.g., dbt, Airflow, Dagster, Snowflake, or Databricks).
  • Vector Expertise: Hands-on experience with at least one major Vector Database and knowledge of similarity search algorithms (HNSW, Cosine Similarity).
  • Search Knowledge: Familiarity with hybrid search techniques (combining semantic search with traditional keyword search like Elasticsearch/BM25).
  • Cloud Infrastructure: Proficiency in managing data workloads on AWS, Azure, or GCP.

Preferred Qualifications

  • Experience with LlamaIndex or LangChain for data ingestion.
  • Knowledge of Graph Databases (e.g., Neo4j) to help agents understand complex relationships between data points.
  • Familiarity with "Data-Centric AI" principles—prioritizing data quality over model size.

Compensation, Benefits and Duration

Minimum Compensation: USD  38,000
Maximum Compensation: USD 133,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post

Skills Required

  • 4+ years in Data Engineering (minimum)
  • At least 1 year focused on data for LLMs or AI/ML
  • Python expertise (Pandas, Pydantic, FastAPI)
  • Experience with modern data stack tools (dbt, Airflow, Dagster, Snowflake, or Databricks)
  • Hands-on experience with at least one Vector Database (e.g., Pinecone, Weaviate, Milvus, Qdrant) and knowledge of similarity search algorithms (HNSW, Cosine Similarity)
  • Familiarity with hybrid search techniques (semantic + keyword search such as Elasticsearch/BM25)
  • Proficiency managing data workloads in cloud environments (AWS, Azure, or GCP)
  • Experience building low-latency real-time data streams (Kafka or Flink)
  • Developing data cleaning workflows to remove noise, PII, and toxicity
  • Metadata engineering and data enrichment for downstream AI use
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The Company
HQ: London
5,017 Employees
Year Founded: 2007

What We Do

Photon.com has emerged as one of the world’s largest and fastest-growing Digital Agencies. We work with 40% of the Fortune 100 on their Digital initiatives and are known for our ability to integrate Strategy Consulting, Creative Design, and Technology at scale. Please visit www.photon.com to learn more about us, how we work, and our customer case studies. Digital Transformation Starts Here.

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